Abstract: A vehicle including a first sensor, a second sensor and a processor is disclosed. The first sensor may be configured to measure first inputs associated with a vehicle wheel, and the second sensor may be configured to measure second inputs associated with the vehicle wheel. The processor may estimate a first distance travelled by the vehicle wheel for a predefined time duration on a vehicle trip based on the first inputs, and a second distance travelled by the vehicle wheel for the predefined time duration on the vehicle trip based on the second inputs. The processor may further calculate a difference between the second distance and the first distance, and perform a predefined action when the difference may be greater than a predefined threshold.

Abstract: Determining classification(s) for object(s) in an environment of autonomous vehicle, and controlling the vehicle based on the determined classification(s). For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be controlled based on determined pose(s) and/or classification(s) for objects in the environment. The control can be based on the pose(s) and/or classification(s) directly, and/or based on movement parameter(s), for the object(s), determined based on the pose(s) and/or classification(s). In many implementations, pose(s) and/or classification(s) of environmental object(s) are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Abstract: Provided herein are methods and compositions for treating a subject having myasthenia gravis using T cells engineered with a chimeric antigen receptor that binds CD19. Also provided herein are methods and compositions for treating a subject having stiff person syndrome using T cells engineered with a chimeric antigen receptor that binds CD19.

Abstract: A microstructured optical fiber sensor for sensing changes in a physical characteristic up to a predetermined temperature is disclosed. The sensor includes a microstructured optical fiber and a fiber Bragg grating formed in the microstructured optical fiber by generating a periodic modulation in the refractive index along a core region of the suspended core. The fiber Bragg grating is configured to produce a band reflection spectra including a fundamental mode and a plurality of higher order modes whose respective wavelengths vary in accordance with changes in the physical characteristic at the core region of the microstructured optical fiber. The microstructured optical fiber is configured to increase the confinement loss of the plurality of higher order modes of the band reflection spectra relative to the fundamental mode.

Abstract: A computer includes a processor and a memory, the memory stores instructions executable by the processor to generate obscured received data from received data by applying at least one Boolean operation to the data and to transmit, via a first communications channel, the obscured received data to a second computer. The executable instructions are additionally to transmit, via a second communications channel, a key to the second computer.

Abstract: Determining an instantaneous vehicle characteristic (e.g., at least one yaw rate) of an additional vehicle that is in addition to a vehicle being autonomously controlled, and adapting autonomous control of the vehicle based on the determined instantaneous vehicle characteristic of the additional vehicle. For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be adapted based on a determined instantaneous vehicle characteristic of the additional vehicle. In many implementations, the instantaneous vehicle characteristics of the additional vehicle are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Abstract: Determining classification(s) for object(s) in an environment of autonomous vehicle, and controlling the vehicle based on the determined classification(s). For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be controlled based on determined pose(s) and/or classification(s) for objects in the environment. The control can be based on the pose(s) and/or classification(s) directly, and/or based on movement parameter(s), for the object(s), determined based on the pose(s) and/or classification(s). In many implementations, pose(s) and/or classification(s) of environmental object(s) are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Abstract: Determining an instantaneous vehicle characteristic (e.g., at least one yaw rate) of an additional vehicle that is in addition to a vehicle being autonomously controlled, and adapting autonomous control of the vehicle based on the determined instantaneous vehicle characteristic of the additional vehicle. For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be adapted based on a determined instantaneous vehicle characteristic of the additional vehicle. In many implementations, the instantaneous vehicle characteristics of the additional vehicle are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Abstract: Determining classification(s) for object(s) in an environment of autonomous vehicle, and controlling the vehicle based on the determined classification(s). For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be controlled based on determined pose(s) and/or classification(s) for objects in the environment. The control can be based on the pose(s) and/or classification(s) directly, and/or based on movement parameter(s), for the object(s), determined based on the pose(s) and/or classification(s). In many implementations, pose(s) and/or classification(s) of environmental object(s) are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Abstract: A system for dynamically adjusting insurance cover and an insurance premium associated with a policy of a client includes at least one computer and a processor. The system calculates a first cover amount by assessing a financial need which would result from occurrence of an insured event during a first period. A first premium is based on the first cover amount. The system then uses adjusted policy data, at least some of which it automatically generates, applicable to a second period of the policy, to calculate a second cover amount by assessing a financial need which would result from occurrence of an insured event during the second period. A second premium is based on the second cover amount. In this way, the system dynamically updates and calculates a premium for each period of the policy based on the financial need of the client in that particular period.

Abstract: A system for determining a reduced insurance premium includes at least one computer and a processor. The system receives a no-lapse discount request relating to a no-lapse period implemented in respect of a policy of a client. The system receives and/or accesses policy data which includes client data and non-client data relevant to the policy. A processor calculates a premium which covers a cost of risk and a cost of expenses associated with the policy and allows for a predefined profit allocation. The processor calculates the premium based on the policy data and on the assumption that the policy will not be cancelled or allowed to lapse during the no-lapse period. The cost of expenses and the predefined profit allocation for the policy are kept unchanged relative to another policy of the same type issued by the insurer, but in respect of which a no-lapse period does not apply.

Abstract: A system for determining a protection level and optimal insurance cover for a client includes at least one computer and a processor. The system receives current cover data for at least one current insurance policy associated with the client or input data in respect of a possible insurance policy for the client. The system generates benchmark cover data associated with proposed insurance cover for the client. A processor compares the current cover data, or the input data, with the benchmark cover data in order to determine a protection level of the client. Protection level data is generated. The protection level data may include a maximum protection level obtainable by the client if a premium indicated by the client as affordable is insufficient for the client to obtain a benchmark cover amount or benchmark cover level.

Abstract: An onboard communication network of a vehicle is monitored to detect a plurality of available messages that include respective cipher-based message authentication codes (CMAC) and that were identified as eligible messages based on having an information entropy greater than a specified threshold. A first message is selected from the plurality of available messages. The CMAC of the selected message is input into a random number generator that outputs a random number seeded by the CMAC of the selected message. Then the random number is provided.

Abstract: A vehicle communication network is monitored to detect a plurality of electronic control units (ECUs). Upon identifying a new ECU in the plurality of ECUs, a highest ECU trip counter is determined from the plurality of ECUs. A global trip counter stored in the memory is updated based on the highest ECU trip counter. The updated trip global trip counter is greater than the highest ECU trip counter. Then a replacement synchronization message is provided to the plurality of ECUs on the vehicle communication network. The replacement synchronization message includes the updated global trip counter.

Abstract: Determining an instantaneous vehicle characteristic (e.g., at least one yaw rate) of an additional vehicle that is in addition to a vehicle being autonomously controlled, and adapting autonomous control of the vehicle based on the determined instantaneous vehicle characteristic of the additional vehicle. For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be adapted based on a determined instantaneous vehicle characteristic of the additional vehicle. In many implementations, the instantaneous vehicle characteristics of the additional vehicle are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Abstract: A system for a vehicle includes a computer, a first electronic control module, and a wired vehicle communications network coupling the computer and the first electronic control module. The computer is programmed to transmit authentication keys to the first electronic control module and a plurality of second electronic control modules via the wired vehicle communications network, encrypt a table of the authentication keys using a first key, store the encrypted table, transmit the encrypted table to the first electronic control module via the wired vehicle communications network, and transmit the encrypted table and the first key to a remote server spaced from the wired vehicle communications network.

Abstract: Determining yaw parameter(s) (e.g., at least one yaw rate) of an additional vehicle that is in addition to a vehicle being autonomously controlled, and adapting autonomous control of the vehicle based on the determined yaw parameter(s) of the additional vehicle. For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be adapted based on a determined yaw rate of the additional vehicle. In many implementations, the yaw parameter(s) of the additional vehicle are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Abstract: A system includes a control module and a local server. The server is programmed to transmit a command to perform an operation to a plurality of vehicles including a vehicle including the control module. The command including a digital signature that is common across the vehicles. The control module is programmed to receive a temporary value; receive the command; decrypt the digital signature in the command with the temporary value; upon verifying the decrypted digital signature, perform the operation; and upon a metric incrementing to a threshold value, prevent decryption of the digital signature with the temporary value.

Abstract: Determining classification(s) for object(s) in an environment of autonomous vehicle, and controlling the vehicle based on the determined classification(s). For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be controlled based on determined pose(s) and/or classification(s) for objects in the environment. The control can be based on the pose(s) and/or classification(s) directly, and/or based on movement parameter(s), for the object(s), determined based on the pose(s) and/or classification(s). In many implementations, pose(s) and/or classification(s) of environmental object(s) are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Abstract: Determining classification(s) for object(s) in an environment of autonomous vehicle, and controlling the vehicle based on the determined classification(s). For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be controlled based on determined pose(s) and/or classification(s) for objects in the environment. The control can be based on the pose(s) and/or classification(s) directly, and/or based on movement parameter(s), for the object(s), determined based on the pose(s) and/or classification(s). In many implementations, pose(s) and/or classification(s) of environmental object(s) are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.